Gradient-based meta-learning is an approach where models learn to adapt quickly to new tasks by optimizing their learning algorithms using gradient descent. This method enables the model to leverage prior knowledge to efficiently learn new tasks with minimal data by updating its parameters in a way that generalizes across tasks.